skip to main content
article
Free Access

On the self-similar nature of Ethernet traffic

Published:11 January 1995Publication History
Skip Abstract Section

Abstract

We demonstrate that Ethernet local area network (LAN) traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal behavior, and that such behavior has serious implications for the design, control, and analysis of high-speed, cell-based networks. Intuitively, the critical characteristic of this self-similar traffic is that there is no natural length of a "burst": at every time scale ranging from a few milliseconds to minutes and hours, similar-looking traffic bursts are evident; we find that aggregating streams of such traffic typically intensifies the self-similarity ("burstiness") instead of smoothing it.Our conclusions are supported by a rigorous statistical analysis of hundreds of millions of high quality Ethernet traffic measurements collected between 1989 and 1992, coupled with a discussion of the underlying mathematical and statistical properties of self-similarity and their relationship with actual network behavior. We also consider some implications for congestion control in high-bandwidth networks and present traffic models based on self-similar stochastic processes that are simple, accurate, and realistic for aggregate traffic.

References

  1. 1. D. Anick, D. Mitra, M.M. Sondhi, "Stochastic Theory of a Data-Handling System with Multiple Sources", Bell System Technical Journal 61, 1871-1894, 1982.Google ScholarGoogle ScholarCross RefCross Ref
  2. 2. J. Beran, "Statistical Methods for Data with Long-Range Dependence", Statistical Science 7, No. 4, 1992.Google ScholarGoogle Scholar
  3. 3. J. Beran, R. Sherman, M. S. Taqqu, W. Willinger, "Variable-Bit-Rate Video Traffic and Long-Range Dependence", accepted for publication in IEEE Trans. on Communication, subject to revisions, 1992.Google ScholarGoogle Scholar
  4. 4. L. M. Berliner, "Statistics, Probability and Chaos", Statistical Science 7, 69-90, 1992.Google ScholarGoogle ScholarCross RefCross Ref
  5. 5. D. R. Cox, "Long-Range Dependence: A Review", in: Statistics: An Appraisal, H. A. David and H. T. David (Eds.), The Iowa State University Press, Ames, Iowa, 55- 74, 1984.Google ScholarGoogle Scholar
  6. 6. R. Dahlhaus, "Efficient Parameter Estimation for Self-Similar Processes", Ann. Statist. 17, 1749-1766, 1989.Google ScholarGoogle ScholarCross RefCross Ref
  7. 7. A. Erramilli, R. P. Singh, "Application of Deterministic Chaotic Maps to Model Packet Traffic in Broadband Networks", Proc. 7th ITC Specialists Seminar, Morristown, NJ, 8.1.1-8.1.3, 1990.Google ScholarGoogle Scholar
  8. 8. H.J. Fowler, W. E. Leland, "Local Area Network Traffic Characteristics, with Implications for Broadband Network Congestion Management", IEEE Journal on Selected Areas in Communications 9, 1139-1149, 1991.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. 9. R. Fox, M. S. Taqqu, "Large-Sample Properties of Parameter Estimates for Strongly Dependent Stationary Gaussian Time Series", Ann. Statist. 14, 517-532, 1986.Google ScholarGoogle ScholarCross RefCross Ref
  10. 10. C.W.J. Granger, R. Joyeux, "An Introduction to Long-Memory Time Series Models and Fractional Differencing", J. Time Series Anal. 1, 15-29, 1980.Google ScholarGoogle ScholarCross RefCross Ref
  11. 11. H. Heffes, D. M. Lucantoni, "A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance", IEEE Journal on Selected Areas in Communications 4, 856-868, 1986.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. 12. J.R.M. Hosking, "Fractional Differencing", Biometrika 68, 165-176, 1981.Google ScholarGoogle ScholarCross RefCross Ref
  13. 13. H.E. Hurst, "Methods of Using Long-Term Storage in Reservoirs", Proc. of the Institution of Civil Engineers, Part 1, 519-577, 1955.Google ScholarGoogle Scholar
  14. 14. R. Jain, S. A. Routhier, "Packet Trains: Measurements and a New Model for Computer Network Traffic", IEEE Journal on Selected Areas in Communications 4, 986-995, 1986.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. 15. W. E. Leland, D. V. Wilson, "High Time-Resolution Measurement and Analysis of LAN Traffic: Implications for LAN interconnection", Proceedings of the IEEE INFOCOM'91, Bal Harbour, FL, 1360-1366, 1991.Google ScholarGoogle Scholar
  16. 16. B. B. Mandelbrot, "Self-Similar Error Clusters in Communication Systems and the Concept of Conditional Stationarity", IEEE Trans. Communications Technology COM-13, 71-90, 1965.Google ScholarGoogle ScholarCross RefCross Ref
  17. 17. B.B. Mandelbrot, "Long-Run Linearity, Locally Gaussian Processes, H-Spectra and Infinite Variances", Intern. Econom. Rev. 10, 82-113, 1969.Google ScholarGoogle ScholarCross RefCross Ref
  18. 18. B. B. Mandelbrot, The Fractal Geometry of Nature, Freeman, New York, 1983.Google ScholarGoogle Scholar
  19. 19. B.B. Mandelbrot, J. W. Van Ness, "Fractional Brownian Motions, Fractional Noises and Applications", SIAM Review 10, 422-437, 1968.Google ScholarGoogle ScholarCross RefCross Ref
  20. 20. B. B. Mandelbrot, J. R. Wallis, "Some Long-Run Properties of Geophysical Records", Water Resources Research 5, 321-340, 1969.Google ScholarGoogle ScholarCross RefCross Ref
  21. 21. K. Meier-Hellstern, P. E. Wirth. Y-L Yan, D. A. Hoeflin, "Traffic Models for ISDN Data Users: Office Automation Application", in: Teletraffic and Datatraffic in a Period of Change (Proc. 13th ITC, Copenhagen, 1991), A. Jensen, V. B. Iversen (Eds.), North Holland, 167-172, 1991.Google ScholarGoogle Scholar
  22. 22. I. Norros, "Studies on a Model for Connectionless Traffic, Based on Fractional Brownian Motion", COST24TD(92)041, 1992.Google ScholarGoogle Scholar
  23. 23. M. S. Taqqu, "A Bibliographical Guide to Self-Similar Processes and Long-Range Dependence", in: Dependence in Probability and Statistics, E. Eberlein and M. S. Taqqu (Eds.), Birkhanser, Basel, 137-165, 1985.Google ScholarGoogle Scholar
  24. 24. M. S. Taqqu, J. B. Levy, "Using Renewal Processes to Generate Long-Range Dependence and High Variability", in: Dependence in Probability and Statistics, E. Eberlein and M. S. Taqqu (Eds.), Progress in Prob. and Stat. Vol. 11, Birkhauser, Boston, 73-89, 1986.Google ScholarGoogle Scholar

Index Terms

  1. On the self-similar nature of Ethernet traffic

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM SIGCOMM Computer Communication Review
          ACM SIGCOMM Computer Communication Review  Volume 25, Issue 1
          Special twenty-fifth anniversary issue. Highlights from 25 years of the Computer Communication Review
          Jan. 1995
          192 pages
          ISSN:0146-4833
          DOI:10.1145/205447
          • Editor:
          • David Oran
          Issue’s Table of Contents

          Copyright © 1995 Copyright is held by the owner/author(s)

          Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 11 January 1995

          Check for updates

          Qualifiers

          • article

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader